Probabilistic algorithms for MEG/EEG source reconstruction using temporal basis functions learned from data.

We present two related probabilistic methods for neural source reconstruction from MEG/EEG data that reduce effects of interference, noise, and correlated sources. Both methods localize source activity using a linear mixture of temporal basis functions (TBFs) learned from the data. In contrast to existing methods that use predetermined TBFs, we compute TBFs from data using a graphical factor analysis based model [Nagarajan, S.S., Attias, H.T., Hild, K.E., Sekihara, K., 2007a. A probabilistic algorithm for robust interference suppression in bioelectromagnetic sensor data. Stat Med 26, 3886–3910], which separates evoked or event-related source activity from ongoing spontaneous background brain activity. Both algorithms compute an optimal weighting of these TBFs at each voxel to provide a spatiotemporal map of activity across the brain and a source image map from the likelihood of a dipole source at each voxel. We explicitly model, with two different robust parameterizations, the contribution from signals outside a voxel of interest. The two models differ in a trade-off of computational speed versus accuracy of learning the unknown interference contributions. Performance in simulations and real data, both with large noise and interference and/or correlated sources, demonstrates significant improvement over existing source localization methods

Occipital alpha activity during stimulus processing gates the information flow to object-selective cortex.

Given the limited processing capabilities of the sensory system, it is essential that attended information is gated to downstream areas, whereas unattended information is blocked. While it has been proposed that alpha band (8–13 Hz) activity serves to route information to downstream regions by inhibiting neuronal processing in task-irrelevant regions, this hypothesis remains untested. Here we investigate how neuronal oscillations detected by electroencephalography in visual areas during working memory encoding serve to gate information reflected in the simultaneously recorded blood-oxygenation-level-dependent (BOLD) signals recorded by functional magnetic resonance imaging in downstream ventral regions. We used a paradigm in which 16 participants were presented with faces and landscapes in the right and left hemifields; one hemifield was attended and the other unattended. We observed that decreased alpha power contralateral to the attended object predicted the BOLD signal representing the attended object in ventral object-selective regions. Furthermore, increased alpha power ipsilateral to the attended object predicted a decrease in the BOLD signal representing the unattended object. We also found that the BOLD signal in the dorsal attention network inversely correlated with visual alpha power. This is the first demonstration, to our knowledge, that oscillations in the alpha band are implicated in the gating of information from the visual cortex to the ventral stream, as reflected in the representationally specific BOLD signal. This link of sensory alpha to downstream activity provides a neurophysiological substrate for the mechanism of selective attention during stimulus processing, which not only boosts the attended information but also suppresses distraction. Although previous studies have shown a relation between the BOLD signal from the dorsal attention network and the alpha band at rest, we demonstrate such a relation during a visuospatial task, indicating that the dorsal attention network exercises top-down control of visual alpha activity

Hemispheric Asymmetry of Globus Pallidus Explains Reward-related Posterior Alpha Modulation in Humans

While subcortical structures such as the basal ganglia (BG) have been widely explored in relation to motor control, recent evidence suggests that their mechanisms extend to the domain of attentional switching. We here investigated the subcortical involvement in reward related top-down control of visual alpha-band oscillations (8 – 13 Hz), which have been consistently linked to the mechanisms supporting the allocation of visual spatial attention. Given that items associated with contextual saliency (e.g. monetary reward or loss) attract attention, it is not surprising that alpha oscillations are further modulated by the saliency properties of the visual items. The executive network controlling such reward-dependent modulations of oscillatory brain activity has yet to be fully elucidated. Although such network has been explored in terms of cortico-cortical interaction, it likely relies also on the contribution of subcortical regions. To uncover this, we investigated whether derived measures of subcortical structural asymmetries could predict interhemispheric modulation of alpha power during a spatial attention task. We show that volumetric hemispheric lateralization of globus pallidus (GP) and thalamus (Th) explains individual hemispheric biases in the ability to modulate posterior alpha power. Importantly, for the GP, this effect became stronger when the value-saliency parings in the task increased. Our findings suggest that the Th and GP in humans are part of a subcortical executive control network, differently involved in modulating posterior alpha activity. Further investigation aimed at uncovering the interaction between subcortical and neocortical attentional networks would provide useful insight in future studies

Occipital Alpha and Gamma Oscillations Support Complementary Mechanisms for Processing Stimulus Value Associations.

Selective attention is reflected neurally in changes in the power of posterior neural oscillations in the alpha (8–12 Hz) and gamma (40–100 Hz) bands. Although a neural mechanism that allows relevant information to be selectively processed has its advantages, it may lead to lucrative or dangerous information going unnoticed. Neural systems are also in place for processing rewarding and punishing information. Here, we examine the interaction between selective attention (left vs. right) and stimulus's learned value associations (neutral, punished, or rewarded) and how they compete for control of posterior neural oscillations. We found that both attention and stimulus–value associations influenced neural oscillations. Whereas selective attention had comparable effects on alpha and gamma oscillations, value associations had dissociable effects on these neural markers of attention. Salient targets (associated with positive and negative outcomes) hijacked changes in alpha power—increasing hemispheric alpha lateralization when salient targets were attended, decreasing it when they were being ignored. In contrast, hemispheric gamma-band lateralization was specifically abolished by negative distractors. Source analysis indicated occipital generators of both attentional and value effects. Thus, posterior cortical oscillations support both the ability to selectively attend while at the same time retaining the ability to remain sensitive to valuable features in the environment. Moreover, the versatility of our attentional system to respond separately to salient from merely positively valued stimuli appears to be carried out by separate neural processes reflected in different frequency bands

Localization of cortico-peripheral coherence with electroencephalography.

Background The analysis of coherent networks from continuous recordings of neural activity with functional MRI or magnetoencephalography has provided important new insights into brain physiology and pathology. Here we assess whether valid localizations of coherent cortical networks can also be obtained from high-resolution electroencephalography (EEG) recordings. Methods EEG was recorded from healthy subjects and from patients with ischemic brain lesions during a tonic hand muscle contraction task and during continuous visual stimulation with an alternating checkerboard. These tasks induce oscillations in the primary hand motor area or in the primary visual cortex, respectively, which are coherent with extracerebral signals (hand muscle electromyogram or visual stimulation frequency). Cortical oscillations were reconstructed with different inverse solutions and the coherence between oscillations at each cortical voxel and the extracerebral signals was calculated. Moreover, simulations of coherent point sources were performed. Results Cortico-muscular coherence was correctly localized to the primary hand motor area and the steady-state visual evoked potentials to the primary visual cortex in all subjects and patients. Sophisticated head models tended to yield better localization accuracy than a single sphere model. A Minimum Variance Beamformer (MVBF) provided more accurate and focal localizations of simulated point sources than an L2 Minimum Norm (MN) inverse solution. In the real datasets, the MN maps had less localization error but were less focal than MVBF maps. Conclusions EEG can localize coherent cortical networks with sufficient accuracy

MEG/EEG source reconstruction, statistical evaluation, and visualization with NUTMEG.

NUTMEG is a source analysis toolbox geared towards cognitive neuroscience researchers using MEG and EEG, including intracranial recordings. Evoked and unaveraged data can be imported to the toolbox for source analysis in either the time or time-frequency domains. NUTMEG offers several variants of adaptive beamformers, probabilistic reconstruction algorithms, as well as minimum-norm techniques to generate functional maps of spatiotemporal neural source activity. Lead fields can be calculated from single and overlapping sphere head models or imported from other software. Group averages and statistics can be calculated as well. In addition to data analysis tools, NUTMEG provides a unique and intuitive graphical interface for visualization of results. Source analyses can be superimposed onto a structural MRI or headshape to provide a convenient visual correspondence to anatomy. These results can also be navigated interactively, with the spatial maps and source time series or spectrogram linked accordingly. Animations can be generated to view the evolution of neural activity over time. NUTMEG can also display brain renderings and perform spatial normalization of functional maps using SPM's engine. As a MATLAB package, the end user may easily link with other toolboxes or add customized functions

The relationship between magnetic and electrophysiological responses to complex tactile stimuli.

Background Magnetoencephalography (MEG) has become an increasingly popular technique for non-invasively characterizing neuromagnetic field changes in the brain at a high temporal resolution. To examine the reliability of the MEG signal, we compared magnetic and electrophysiological responses to complex natural stimuli from the same animals. We examined changes in neuromagnetic fields, local field potentials (LFP) and multi-unit activity (MUA) in macaque monkey primary somatosensory cortex that were induced by varying the rate of mechanical stimulation. Stimuli were applied to the fingertips with three inter-stimulus intervals (ISIs): 0.33s, 1s and 2s. Results Signal intensity was inversely related to the rate of stimulation, but to different degrees for each measurement method. The decrease in response at higher stimulation rates was significantly greater for MUA than LFP and MEG data, while no significant difference was observed between LFP and MEG recordings. Furthermore, response latency was the shortest for MUA and the longest for MEG data. Conclusion The MEG signal is an accurate representation of electrophysiological responses to complex natural stimuli. Further, the intensity and latency of the MEG signal were better correlated with the LFP than MUA data suggesting that the MEG signal reflects primarily synaptic currents rather than spiking activity. These differences in latency could be attributed to differences in the extent of spatial summation and/or differential laminar sensitivity

Changes in brain network activity during working memory tasks: a magnetoencephalography study.

In this study, we elucidate the changes in neural oscillatory processes that are induced by simple working memory tasks. A group of eight subjects took part in modified versions of the N-back and Sternberg working memory paradigms. Magnetoencephalography (MEG) data were recorded, and subsequently processed using beamformer based source imaging methodology. Our study shows statistically significant increases in θ oscillations during both N-back and Sternberg tasks. These oscillations were shown to originate in the medial frontal cortex, and further to scale with memory load. We have also shown that increases in θ oscillations are accompanied by decreases in β and γ band oscillations at the same spatial coordinate. These decreases were most prominent in the 20–40 Hz frequency range, although spectral analysis showed that γ band power decrease extends up to at least 80 Hz. β/γ Power decrease also scales with memory load. Whilst θ increases were predominately observed in the medial frontal cortex, β/γ decreases were associated with other brain areas, including nodes of the default mode network (for the N-back task) and areas associated with language processing (for the Sternberg task). These observations are in agreement with intracranial EEG and fMRI studies. Finally, we have shown an intimate relationship between changes in β/γ band oscillatory power at spatially separate network nodes, implying that activity in these nodes is not reflective of uni-modal task driven changes in spatially separate brain regions, but rather represents correlated network activity. The utility of MEG as a non-invasive means to measure neural oscillatory modulation has been demonstrated and future studies employing this technology have the potential to gain a better understanding of neural oscillatory processes, their relationship to functional and effective connectivity, and their correspondence to BOLD fMRI

The Temporal Signature of Memories: Identification of a General Mechanism for Dynamic Memory Replay in Humans

Reinstatement of dynamic memories requires the replay of neural patterns that unfold over time in a similar manner as during perception. However, little is known about the mechanisms that guide such a temporally structured replay in humans, because previous studies used either unsuitable methods or paradigms to address this question. Here, we overcome these limitations by developing a new analysis method to detect the replay of temporal patterns in a paradigm that requires participants to mentally replay short sound or video clips. We show that memory reinstatement is accompanied by a decrease of low-frequency (8 Hz) power, which carries a temporal phase signature of the replayed stimulus. These replay effects were evident in the visual as well as in the auditory domain and were localized to sensory-specific regions. These results suggest low-frequency phase to be a domain-general mechanism that orchestrates dynamic memory replay in humans

Individual Human Brain Areas Can Be Identified from Their Characteristic Spectral Activation Fingerprints

The human brain can be parcellated into diverse anatomical areas. We investigated whether rhythmic brain activity in these areas is characteristic and can be used for automatic classification. To this end, resting-state MEG data of 22 healthy adults was analysed. Power spectra of 1-s long data segments for atlas-defined brain areas were clustered into spectral profiles (“fingerprints”), using k-means and Gaussian mixture (GM) modelling. We demonstrate that individual areas can be identified from these spectral profiles with high accuracy. Our results suggest that each brain area engages in different spectral modes that are characteristic for individual areas. Clustering of brain areas according to similarity of spectral profiles reveals well-known brain networks. Furthermore, we demonstrate task-specific modulations of auditory spectral profiles during auditory processing. These findings have important implications for the classification of regional spectral activity and allow for novel approaches in neuroimaging and neurostimulation in health and disease